University of Hertfordshire

Using data mining to refine digital behaviour change interventions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Documents

  • Nathaniel Charlton
  • John Kingston
  • Ben Fletcher
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Original languageEnglish
Title of host publicationDH '17: Proceedings of the 2017 International Conference on Digital Health
PublisherAssociation for Computing Machinery (ACM)
Pages90–98
Number of pages9
ISBN (Print)9781450352499
DOIs
Publication statusPublished - 31 Jul 2017
Event7th International conference on Digital Health 2017 - LONDON, United Kingdom
Duration: 3 Jul 20175 Jul 2017
http://www.digra.org/cfp-7th-international-conference-on-digital-health/

Conference

Conference7th International conference on Digital Health 2017
CountryUnited Kingdom
CityLONDON
Period3/07/175/07/17
Internet address

Abstract

Do Something Different (DSD) behaviour change interventions are digitally delivered programmes designed to help people improve their health and wellbeing by adopting healthier habits. In addition to content addressing specific issues, such as diet, smoking and stress reduction, DSD interventions contain a core component promoting behavioural flexibility. This component helps people practice behaving in ways they currently do not, such as assertively, proactively or spontaneously, and is based on a model developed by psychologists researching the connections between behavioural flexibility and wellbeing.

This paper describes how we have used data mining techniques to optimise the design of DSD interventions, in particular the behavioural flexibility component. We present correlation networks and regression models obtained using pre- and post-intervention questionnaire data from 15,550 people who have participated in a DSD intervention delivered by email, SMS or smartphone app. We explain how these results led us to a clearer understanding of the connections between behaviour and wellbeing, using which we have optimised DSD interventions, ensuring that participants concentrate on developing the behaviours that are likely to benefit them the most.

Additionally we have used logistic regression to fit a propensity score model, which models how likely it is that each person in the dataset will complete the post-intervention questionnaire, based on their pre-intervention questionnaire data. When we stratify our dataset using these propensity scores, we find that the kind of people who are the least likely to tell us they have completed the intervention, by answering the post-intervention questionnaire, are also the kind of people who will experience the biggest increase in wellbeing from a completed programme.

ID: 20424970